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The direpack package establishes a set of modern statistical dimensionality reduction techniques into the Python universe as a single, consistent package. Several of the methods included are only available as open source through direpack, whereas the package also offers competitive Python implementations of methods previously only available in other programming languages. In its present version, the package is structured in three subpackages for different approaches to dimensionality reduction: projection pursuit, sufficient dimension reduction and robust M estimators. As a corollary, the package also provides access to regularized regression estimators based on these reduced dimension spaces, as well as a set of classical and robust preprocessing utilities, including very recent developments such as generalized spatial signs. Finally, direpack has been written to be consistent with the scikit-learn API, such that the estimators can flawlessly be included into (statistical and/or machine) learning pipelines in that framework.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).